Data-driven modeling of Alzheimer disease pathogenesis

Alzheimer Disease (AD) is the most prevalent form of dementia and the sixth leading cause of death in developed world. A substantial amount of data concerning the pathogenesis of this neurological disorder is available, but the complexity of the interactions they reveal makes it difficult to reason about them. This paper describes a computational model that represents known facts concerning AD pathophysiology and demonstrates the implications of those facts in the aggregate. The computational model is written in a mathematical language known as Maude. Because a Maude specification is an executable mathematical theory, it can be used not only to simulate but also to logically analyze the system it models. This model is based on the amyloid hypothesis, which posits that AD results from the build-up of the peptide beta-amyloid. The AD model represents beta-amyloid regulation, and shows through model analysis how that regulation can be disrupted through the interaction of pathological processes such as cerebrovascular insufficiency, inflammation, and oxidative stress. The model demonstrates many other effects that depend in complex ways on interactions between elements. It also shows how treatments directed at multiple targets could be more effective at reducing beta-amyloid than single-target therapies, and it makes several experimentally testable predictions. The work demonstrates that modeling AD as an executable mathematical theory using a specification language such as Maude is a viable adjunct to experiment, which allows insights and predictions to be derived that take more of the relevant biology into account than would be possible without the aid of the computational model.